Overview

Dataset statistics

Number of variables13
Number of observations1500
Missing cells57
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory152.5 KiB
Average record size in memory104.1 B

Variable types

Numeric9
Categorical4

Warnings

Feat11 is highly correlated with PotentialBuyerHyperCryptoHigh correlation
PotentialBuyerHyperCrypto is highly correlated with Feat11High correlation
Feat5 has 57 (3.8%) missing values Missing
Feat0 has unique values Unique
Feat5 has 70 (4.7%) zeros Zeros
Feat9 has 186 (12.4%) zeros Zeros
Feat11 has 122 (8.1%) zeros Zeros

Reproduction

Analysis started2022-04-16 13:14:47.055400
Analysis finished2022-04-16 13:15:12.533491
Duration25.48 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Feat0
Real number (ℝ≥0)

UNIQUE

Distinct1500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5013743805
Minimum0.003947495
Maximum0.999966816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2022-04-16T18:15:12.670415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.003947495
5-th percentile0.0542402929
Q10.2471660605
median0.503232114
Q30.7548537312
95-th percentile0.943482549
Maximum0.999966816
Range0.996019321
Interquartile range (IQR)0.5076876707

Descriptive statistics

Standard deviation0.2902779922
Coefficient of variation (CV)0.5789645493
Kurtosis-1.232081459
Mean0.5013743805
Median Absolute Deviation (MAD)0.2524017325
Skewness-0.002506645985
Sum752.0615708
Variance0.08426131278
MonotonicityNot monotonic
2022-04-16T18:15:12.963610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2755215991
 
0.1%
0.8928052571
 
0.1%
0.0831524491
 
0.1%
0.8056008351
 
0.1%
0.2061017731
 
0.1%
0.1423661341
 
0.1%
0.1118526351
 
0.1%
0.3970502591
 
0.1%
0.4867738231
 
0.1%
0.9003098291
 
0.1%
Other values (1490)1490
99.3%
ValueCountFrequency (%)
0.0039474951
0.1%
0.0041285221
0.1%
0.0043461311
0.1%
0.0054492071
0.1%
0.005928271
0.1%
0.0068301711
0.1%
0.0072996531
0.1%
0.0084657251
0.1%
0.0090338341
0.1%
0.0115736261
0.1%
ValueCountFrequency (%)
0.9999668161
0.1%
0.998744991
0.1%
0.9984055381
0.1%
0.9977084211
0.1%
0.9971584051
0.1%
0.9963738641
0.1%
0.9946603491
0.1%
0.9942680521
0.1%
0.9939783091
0.1%
0.9939766221
0.1%

Feat1
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
0
767 
1
733 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0767
51.1%
1733
48.9%

Length

2022-04-16T18:15:13.418616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-16T18:15:13.550188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0767
51.1%
1733
48.9%

Most occurring characters

ValueCountFrequency (%)
0767
51.1%
1733
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0767
51.1%
1733
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common1500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0767
51.1%
1733
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0767
51.1%
1733
48.9%

Feat2
Real number (ℝ≥0)

Distinct781
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.931026667
Minimum0
Maximum10
Zeros3
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2022-04-16T18:15:13.734396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4195
Q12.52
median4.965
Q37.3625
95-th percentile9.3805
Maximum10
Range10
Interquartile range (IQR)4.8425

Descriptive statistics

Standard deviation2.846412957
Coefficient of variation (CV)0.5772455007
Kurtosis-1.155321903
Mean4.931026667
Median Absolute Deviation (MAD)2.415
Skewness-0.02173731036
Sum7396.54
Variance8.102066724
MonotonicityNot monotonic
2022-04-16T18:15:14.018373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.36
 
0.4%
2.896
 
0.4%
7.15
 
0.3%
75
 
0.3%
7.55
 
0.3%
9.185
 
0.3%
2.75
 
0.3%
3.335
 
0.3%
5.895
 
0.3%
4.885
 
0.3%
Other values (771)1448
96.5%
ValueCountFrequency (%)
03
0.2%
0.011
 
0.1%
0.021
 
0.1%
0.031
 
0.1%
0.042
0.1%
0.052
0.1%
0.062
0.1%
0.073
0.2%
0.083
0.2%
0.091
 
0.1%
ValueCountFrequency (%)
102
0.1%
9.971
 
0.1%
9.951
 
0.1%
9.932
0.1%
9.922
0.1%
9.93
0.2%
9.881
 
0.1%
9.872
0.1%
9.852
0.1%
9.841
 
0.1%

Feat3
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
D
758 
A
376 
B
366 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1500
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowB
4th rowD
5th rowA

Common Values

ValueCountFrequency (%)
D758
50.5%
A376
25.1%
B366
24.4%

Length

2022-04-16T18:15:14.518757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-16T18:15:14.665363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
d758
50.5%
a376
25.1%
b366
24.4%

Most occurring characters

ValueCountFrequency (%)
D758
50.5%
A376
25.1%
B366
24.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1500
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D758
50.5%
A376
25.1%
B366
24.4%

Most occurring scripts

ValueCountFrequency (%)
Latin1500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D758
50.5%
A376
25.1%
B366
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D758
50.5%
A376
25.1%
B366
24.4%

Feat4
Real number (ℝ≥0)

Distinct101
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5043266667
Minimum0
Maximum1
Zeros9
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2022-04-16T18:15:14.849979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0495
Q10.25
median0.5
Q30.77
95-th percentile0.95
Maximum1
Range1
Interquartile range (IQR)0.52

Descriptive statistics

Standard deviation0.2906279231
Coefficient of variation (CV)0.5762691967
Kurtosis-1.244915929
Mean0.5043266667
Median Absolute Deviation (MAD)0.26
Skewness-0.007408386684
Sum756.49
Variance0.08446458968
MonotonicityNot monotonic
2022-04-16T18:15:15.149345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7825
 
1.7%
0.1223
 
1.5%
0.3323
 
1.5%
0.1422
 
1.5%
0.1822
 
1.5%
0.8121
 
1.4%
0.1621
 
1.4%
0.6421
 
1.4%
0.3820
 
1.3%
0.7620
 
1.3%
Other values (91)1282
85.5%
ValueCountFrequency (%)
09
0.6%
0.0113
0.9%
0.0215
1.0%
0.0318
1.2%
0.0420
1.3%
0.057
 
0.5%
0.069
0.6%
0.077
 
0.5%
0.0813
0.9%
0.0911
0.7%
ValueCountFrequency (%)
19
0.6%
0.9917
1.1%
0.9810
0.7%
0.9713
0.9%
0.9614
0.9%
0.9517
1.1%
0.9416
1.1%
0.9318
1.2%
0.9211
0.7%
0.9118
1.2%

Feat5
Real number (ℝ≥0)

MISSING
ZEROS

Distinct11
Distinct (%)0.8%
Missing57
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean5.024948025
Minimum0
Maximum10
Zeros70
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2022-04-16T18:15:15.377677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37.5
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation2.904525842
Coefficient of variation (CV)0.5780210716
Kurtosis-1.131014552
Mean5.024948025
Median Absolute Deviation (MAD)2
Skewness-0.02053310658
Sum7251
Variance8.436270368
MonotonicityNot monotonic
2022-04-16T18:15:15.579314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5155
10.3%
8154
10.3%
6152
10.1%
1149
9.9%
7143
9.5%
4140
9.3%
2137
9.1%
3136
9.1%
9130
8.7%
1077
5.1%
ValueCountFrequency (%)
070
4.7%
1149
9.9%
2137
9.1%
3136
9.1%
4140
9.3%
5155
10.3%
6152
10.1%
7143
9.5%
8154
10.3%
9130
8.7%
ValueCountFrequency (%)
1077
5.1%
9130
8.7%
8154
10.3%
7143
9.5%
6152
10.1%
5155
10.3%
4140
9.3%
3136
9.1%
2137
9.1%
1149
9.9%

Feat6
Real number (ℝ≥0)

Distinct101
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.15866667
Minimum0
Maximum100
Zeros14
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2022-04-16T18:15:15.834765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median50
Q375
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.1559749
Coefficient of variation (CV)0.5812749188
Kurtosis-1.179236481
Mean50.15866667
Median Absolute Deviation (MAD)25.5
Skewness-0.009910181459
Sum75238
Variance850.0708721
MonotonicityNot monotonic
2022-04-16T18:15:16.106280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3923
 
1.5%
9523
 
1.5%
6022
 
1.5%
9322
 
1.5%
6822
 
1.5%
8022
 
1.5%
2322
 
1.5%
9722
 
1.5%
2022
 
1.5%
8920
 
1.3%
Other values (91)1280
85.3%
ValueCountFrequency (%)
014
0.9%
118
1.2%
213
0.9%
315
1.0%
416
1.1%
518
1.2%
620
1.3%
710
0.7%
814
0.9%
910
0.7%
ValueCountFrequency (%)
1009
 
0.6%
9913
0.9%
9815
1.0%
9722
1.5%
9618
1.2%
9523
1.5%
9410
0.7%
9322
1.5%
9212
0.8%
9115
1.0%

Feat7
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
3
378 
1
368 
0
368 
2
346 
9999
40 

Length

Max length4
Median length1
Mean length1.08
Min length1

Characters and Unicode

Total characters1620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3378
25.2%
1368
24.5%
0368
24.5%
2346
23.1%
999940
 
2.7%

Length

2022-04-16T18:15:16.591046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-16T18:15:16.741047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3378
25.2%
1368
24.5%
0368
24.5%
2346
23.1%
999940
 
2.7%

Most occurring characters

ValueCountFrequency (%)
3378
23.3%
1368
22.7%
0368
22.7%
2346
21.4%
9160
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3378
23.3%
1368
22.7%
0368
22.7%
2346
21.4%
9160
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3378
23.3%
1368
22.7%
0368
22.7%
2346
21.4%
9160
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3378
23.3%
1368
22.7%
0368
22.7%
2346
21.4%
9160
9.9%

Feat8
Real number (ℝ≥0)

Distinct101
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.654
Minimum0
Maximum100
Zeros14
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2022-04-16T18:15:16.950054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q128
median52
Q375
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)47

Descriptive statistics

Standard deviation28.4904795
Coefficient of variation (CV)0.5515638576
Kurtosis-1.129577952
Mean51.654
Median Absolute Deviation (MAD)23
Skewness-0.01766513142
Sum77481
Variance811.7074223
MonotonicityNot monotonic
2022-04-16T18:15:17.451309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4527
 
1.8%
6424
 
1.6%
9522
 
1.5%
4022
 
1.5%
9022
 
1.5%
2921
 
1.4%
3821
 
1.4%
7221
 
1.4%
5821
 
1.4%
4620
 
1.3%
Other values (91)1279
85.3%
ValueCountFrequency (%)
014
0.9%
116
1.1%
210
0.7%
37
0.5%
43
 
0.2%
517
1.1%
610
0.7%
710
0.7%
811
0.7%
913
0.9%
ValueCountFrequency (%)
10019
1.3%
9914
0.9%
9817
1.1%
9712
0.8%
9618
1.2%
9522
1.5%
9416
1.1%
9314
0.9%
9219
1.3%
9112
0.8%

Feat9
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.480666667
Minimum0
Maximum7
Zeros186
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2022-04-16T18:15:17.665497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.252037745
Coefficient of variation (CV)0.6470133341
Kurtosis-1.198547923
Mean3.480666667
Median Absolute Deviation (MAD)2
Skewness0.001152307014
Sum5221
Variance5.071674005
MonotonicityNot monotonic
2022-04-16T18:15:17.857480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2204
13.6%
3201
13.4%
6196
13.1%
5188
12.5%
4187
12.5%
0186
12.4%
1170
11.3%
7168
11.2%
ValueCountFrequency (%)
0186
12.4%
1170
11.3%
2204
13.6%
3201
13.4%
4187
12.5%
5188
12.5%
6196
13.1%
7168
11.2%
ValueCountFrequency (%)
7168
11.2%
6196
13.1%
5188
12.5%
4187
12.5%
3201
13.4%
2204
13.6%
1170
11.3%
0186
12.4%

Feat10
Real number (ℝ≥0)

Distinct766
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.7106667
Minimum0
Maximum999
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2022-04-16T18:15:18.105121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48.95
Q1246.5
median514
Q3756.25
95-th percentile947.05
Maximum999
Range999
Interquartile range (IQR)509.75

Descriptive statistics

Standard deviation290.5058922
Coefficient of variation (CV)0.5801871451
Kurtosis-1.2242305
Mean500.7106667
Median Absolute Deviation (MAD)253
Skewness-0.01742652095
Sum751066
Variance84393.6734
MonotonicityNot monotonic
2022-04-16T18:15:18.374179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8147
 
0.5%
8697
 
0.5%
367
 
0.5%
8826
 
0.4%
5356
 
0.4%
3976
 
0.4%
5246
 
0.4%
9136
 
0.4%
8116
 
0.4%
7206
 
0.4%
Other values (756)1437
95.8%
ValueCountFrequency (%)
01
 
0.1%
13
0.2%
21
 
0.1%
33
0.2%
41
 
0.1%
51
 
0.1%
61
 
0.1%
71
 
0.1%
91
 
0.1%
104
0.3%
ValueCountFrequency (%)
9991
 
0.1%
9981
 
0.1%
9961
 
0.1%
9954
0.3%
9942
0.1%
9931
 
0.1%
9921
 
0.1%
9911
 
0.1%
9891
 
0.1%
9871
 
0.1%

Feat11
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.937333333
Minimum0
Maximum10
Zeros122
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2022-04-16T18:15:18.614252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.127074024
Coefficient of variation (CV)0.6333528269
Kurtosis-1.186826955
Mean4.937333333
Median Absolute Deviation (MAD)3
Skewness0.06746115879
Sum7406
Variance9.77859195
MonotonicityNot monotonic
2022-04-16T18:15:18.835629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3147
9.8%
4147
9.8%
2145
9.7%
1144
9.6%
10141
9.4%
6140
9.3%
5138
9.2%
7128
8.5%
8125
8.3%
9123
8.2%
ValueCountFrequency (%)
0122
8.1%
1144
9.6%
2145
9.7%
3147
9.8%
4147
9.8%
5138
9.2%
6140
9.3%
7128
8.5%
8125
8.3%
9123
8.2%
ValueCountFrequency (%)
10141
9.4%
9123
8.2%
8125
8.3%
7128
8.5%
6140
9.3%
5138
9.2%
4147
9.8%
3147
9.8%
2145
9.7%
1144
9.6%

PotentialBuyerHyperCrypto
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
0
1256 
1
244 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01256
83.7%
1244
 
16.3%

Length

2022-04-16T18:15:19.291358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-16T18:15:19.425273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
01256
83.7%
1244
 
16.3%

Most occurring characters

ValueCountFrequency (%)
01256
83.7%
1244
 
16.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01256
83.7%
1244
 
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common1500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01256
83.7%
1244
 
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01256
83.7%
1244
 
16.3%

Interactions

2022-04-16T18:14:52.479928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:52.713330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:53.105692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:53.333215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:53.552808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:53.806906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:54.061748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:54.306776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:54.527760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:54.811172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:55.044753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:55.272701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:55.525784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:55.747404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:55.974931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:56.190500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:56.418576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:56.632950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:56.843877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:57.062707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:57.292799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:57.519148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:57.746739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:57.974520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:58.199792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:58.431760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:58.648970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:58.867886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:59.080724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:59.290451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:59.505434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:59.731242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:14:59.942179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:00.314735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:00.542201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:00.761548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:00.971583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:01.184054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:01.395190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:01.613689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:01.823708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:02.044981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:02.260172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:02.487501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:02.697572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:02.908571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:03.119465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:03.330301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:03.547112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:03.765374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:03.982258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:04.194215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:04.416910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:04.629483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:04.853396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:05.086478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:05.315538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:05.546151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:05.782417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:06.026598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:06.281392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:06.522701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:06.760461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:07.022312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:07.275015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:07.512699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:07.726026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:07.964683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:08.202238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:08.413944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:08.636251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:09.023707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:09.264274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:09.484038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:09.706761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:09.928395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:10.147946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:10.360742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:10.605792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:10.848074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T18:15:11.083913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-04-16T18:15:19.570122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-16T18:15:19.942331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-16T18:15:20.420883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-16T18:15:20.833279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-04-16T18:15:21.231314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-04-16T18:15:11.503364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-16T18:15:12.099423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-16T18:15:12.339080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Feat0Feat1Feat2Feat3Feat4Feat5Feat6Feat7Feat8Feat9Feat10Feat11PotentialBuyerHyperCrypto
00.27552212.77D0.996.0893467468100
10.36542914.75D0.643.0812277411
20.28435306.95B0.347.093129093370
30.35686712.87D0.805.039361725420
40.46289602.81A0.824.05375480570
50.07212500.33D0.41NaN9505588910
60.41192111.72A0.272.056128513930
70.87668703.21D0.715.040047635070
80.90836302.52D0.509.022070021331
90.44510913.70A0.282.070270451710

Last rows

Feat0Feat1Feat2Feat3Feat4Feat5Feat6Feat7Feat8Feat9Feat10Feat11PotentialBuyerHyperCrypto
14900.30889310.04D0.199.052264635680
14910.24455000.20D0.678.05319438070
14920.96887017.00A0.752.080142323480
14930.53463005.01B0.6110.071049593180
14940.06334202.24D0.765.076082668250
14950.98206010.18D0.607.0601763273100
14960.00730004.76A0.761.082316493270
14970.81542104.75A0.905.061277038541
14980.02240917.86D0.568.035290213170
14990.44835508.17B0.120.01625833170